## Warning: package 'rmdformats' was built under R version 3.5.2

R hakkında

R generation

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x

R yükleme

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R-project

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R zor şeyler için kolay, kolay şeyler için zor

R makes easy things hard, and hard things easy
Aynı şeyi çok fazla şekilde yapmak mümkün

R Syntax Comparison::CHEAT SHEET

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R paketleri

Neden paketler var

I love the #rstats community.
Someone is like, “oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It's called pbplyr.”
What a tribe.
— Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) July 3, 2018

https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/

Paketleri nereden bulabiliriz

Available CRAN Packages By Name
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RecommendR
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Awesome R
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Kendi paket evrenini oluştur

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R için yardım bulma

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Google uygun anahtar kelime

How I use #rstats
h/t @ThePracticalDev pic.twitter.com/erRnTG0Ujr
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Awesome Cheatsheet https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/

Awesome R

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https://awesome-r.com/

Twitter

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Reproducible Examples

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— ZhiYang (@zhiiiyang) October 18, 2018

R paket yükleme

install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")

R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

RStudio ile veri yükleme

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Excel

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csv

Veriyi görüntüleme

Spreadsheet users using #rstats: where's the data?#rstats users using spreadsheets: where's the code?
— Leonard Kiefer (@lenkiefer) July 7, 2018

View(data)

data

head

tail

glimpse

str

skimr::skim()

Veriyi değiştirme

Veriyi kod ile değiştirelim

Veriyi eklentilerle değiştirme

RStudio aracılığıyla recode

questionr paketi kullanılacak

https://juba.github.io/questionr/articles/recoding_addins.html

Basit tanımlayıcı istatistikler

summary()

mean

median

min

max

sd

table()

library(readr)
irisdata <- read_csv("data/iris.csv")

jmv::descriptives(data = irisdata, vars = "Sepal.Length", splitBy = "Species", 
    freq = TRUE, hist = TRUE, dens = TRUE, bar = TRUE, box = TRUE, violin = TRUE, 
    dot = TRUE, mode = TRUE, sum = TRUE, sd = TRUE, variance = TRUE, range = TRUE, 
    se = TRUE, skew = TRUE, kurt = TRUE, quart = TRUE, pcEqGr = TRUE)


 DESCRIPTIVES

 Descriptives                                          
 ───────────────────────────────────────────────────── 
                          Species       Sepal.Length   
 ───────────────────────────────────────────────────── 
   N                      setosa                  50   
                          versicolor              50   
                          virginica               50   
   Missing                setosa                   0   
                          versicolor               0   
                          virginica                0   
   Mean                   setosa                5.01   
                          versicolor            5.94   
                          virginica             6.59   
   Std. error mean        setosa              0.0498   
                          versicolor          0.0730   
                          virginica           0.0899   
   Median                 setosa                5.00   
                          versicolor            5.90   
                          virginica             6.50   
   Mode                   setosa                5.00   
                          versicolor            5.50   
                          virginica             6.30   
   Sum                    setosa                 250   
                          versicolor             297   
                          virginica              329   
   Standard deviation     setosa               0.352   
                          versicolor           0.516   
                          virginica            0.636   
   Variance               setosa               0.124   
                          versicolor           0.266   
                          virginica            0.404   
   Range                  setosa                1.50   
                          versicolor            2.10   
                          virginica             3.00   
   Minimum                setosa                4.30   
                          versicolor            4.90   
                          virginica             4.90   
   Maximum                setosa                5.80   
                          versicolor            7.00   
                          virginica             7.90   
   Skewness               setosa               0.120   
                          versicolor           0.105   
                          virginica            0.118   
   Std. error skewness    setosa               0.337   
                          versicolor           0.337   
                          virginica            0.337   
   Kurtosis               setosa              -0.253   
                          versicolor          -0.533   
                          virginica           0.0329   
   Std. error kurtosis    setosa               0.662   
                          versicolor           0.662   
                          virginica            0.662   
   25th percentile        setosa                4.80   
                          versicolor            5.60   
                          virginica             6.23   
   50th percentile        setosa                5.00   
                          versicolor            5.90   
                          virginica             6.50   
   75th percentile        setosa                5.20   
                          versicolor            6.30   
                          virginica             6.90   
 ─────────────────────────────────────────────────────

# install.packages('scatr')

scatr::scat(data = irisdata, x = "Sepal.Length", y = "Sepal.Width", group = "Species", 
    marg = "dens", line = "linear", se = TRUE)

summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

library(summarytools)
summarytools::freq(iris$Species, style = "rmarkdown")

Frequencies

Variable: iris$Species
Type: Factor (unordered)

	Freq	% Valid	% Valid Cum.	% Total	% Total Cum.
setosa	50	33.33	33.33	33.33	33.33
versicolor	50	33.33	66.67	33.33	66.67
virginica	50	33.33	100.00	33.33	100.00
<NA>	0			0.00	100.00
Total	150	100.00	100.00	100.00	100.00

summarytools::freq(iris$Species, report.nas = FALSE, style = "rmarkdown", omit.headings = TRUE)

	Freq	%	% Cum.
setosa	50	33.33	33.33
versicolor	50	33.33	66.67
virginica	50	33.33	100.00
Total	150	100.00	100.00

with(tobacco, print(ctable(smoker, diseased), method = "render"))

Cross-Tabulation / Row Proportions

Variables: smoker * diseased
Data Frame: tobacco

	diseased
smoker	Yes	No	Total
Yes	125 (41.95%)	173 (58.05%)	298 (100.00%)
No	99 (14.10%)	603 (85.90%)	702 (100.00%)
Total	224 (22.40%)	776 (77.60%)	1000 (100.00%)

Generated by summarytools 0.8.8 (R version 3.5.1)
2019-01-14

with(tobacco, print(ctable(smoker, diseased, prop = "n", totals = FALSE), omit.headings = TRUE, 
    method = "render"))

	diseased
smoker	Yes	No
Yes	125	173
No	99	603

Generated by summarytools 0.8.8 (R version 3.5.1)
2019-01-14

summarytools::descr(iris, style = "rmarkdown")

Descriptive Statistics

Data Frame: iris
N: 150

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
Mean	5.84	3.06	3.76	1.20
Std.Dev	0.83	0.44	1.77	0.76
Min	4.30	2.00	1.00	0.10
Q1	5.10	2.80	1.60	0.30
Median	5.80	3.00	4.35	1.30
Q3	6.40	3.30	5.10	1.80
Max	7.90	4.40	6.90	2.50
MAD	1.04	0.44	1.85	1.04
IQR	1.30	0.50	3.50	1.50
CV	0.14	0.14	0.47	0.64
Skewness	0.31	0.31	-0.27	-0.10
SE.Skewness	0.20	0.20	0.20	0.20
Kurtosis	-0.61	0.14	-1.42	-1.36
N.Valid	150.00	150.00	150.00	150.00
Pct.Valid	100.00	100.00	100.00	100.00

descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE, 
    omit.headings = TRUE, style = "rmarkdown")

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	5.84	0.83	4.30	5.80	7.90
Sepal.Width	3.06	0.44	2.00	3.00	4.40
Petal.Length	3.76	1.77	1.00	4.35	6.90
Petal.Width	1.20	0.76	0.10	1.30	2.50

# view(dfSummary(iris))

dfSummary(tobacco, plain.ascii = FALSE, style = "grid")

Data Frame Summary

tobacco
N: 1000

No	Variable	Stats / Values	Freqs (% of Valid)	Text Graph	Valid	Missing
1	gender [factor]	1. F 2. M	489 (50.0%) 489 (50.0%)	IIIIIIIIIIIIIIII IIIIIIIIIIIIIIII	978 (97.8%)	22 (2.2%)
2	age [numeric]	mean (sd) : 49.6 (18.29) min < med < max : 18 < 50 < 80 IQR (CV) : 32 (0.37)	63 distinct values		975 (97.5%)	25 (2.5%)
3	age.gr [factor]	1. 18-34 2. 35-50 3. 51-70 4. 71 +	258 (26.5%) 241 (24.7%) 317 (32.5%) 159 (16.3%)	IIIIIIIIIIIII IIIIIIIIIIII IIIIIIIIIIIIIIII IIIIIIII	975 (97.5%)	25 (2.5%)
4	BMI [numeric]	mean (sd) : 25.73 (4.49) min < med < max : 8.83 < 25.62 < 39.44 IQR (CV) : 5.72 (0.17)	974 distinct values		974 (97.4%)	26 (2.6%)
5	smoker [factor]	1. Yes 2. No	298 (29.8%) 702 (70.2%)	IIIIII IIIIIIIIIIIIIIII	1000 (100%)	0 (0%)
6	cigs.per.day [numeric]	mean (sd) : 6.78 (11.88) min < med < max : 0 < 0 < 40 IQR (CV) : 11 (1.75)	37 distinct values		965 (96.5%)	35 (3.5%)
7	diseased [factor]	1. Yes 2. No	224 (22.4%) 776 (77.6%)	IIII IIIIIIIIIIIIIIII	1000 (100%)	0 (0%)
8	disease [character]	1. Hypertension 2. Cancer 3. Cholesterol 4. Heart 5. Pulmonary 6. Musculoskeletal 7. Diabetes 8. Hearing 9. Digestive 10. Hypotension [ 3 others ]	36 (16.2%) 34 (15.3%) 21 ( 9.5%) 20 ( 9.0%) 20 ( 9.0%) 19 ( 8.6%) 14 ( 6.3%) 14 ( 6.3%) 12 ( 5.4%) 11 ( 5.0%) 21 ( 9.5%)	IIIIIIIIIIIIIIII IIIIIIIIIIIIIII IIIIIIIII IIIIIIII IIIIIIII IIIIIIII IIIIII IIIIII IIIII IIII IIIIIIIII	222 (22.2%)	778 (77.8%)
9	samp.wgts [numeric]	mean (sd) : 1 (0.08) min < med < max : 0.86 < 1.04 < 1.06 IQR (CV) : 0.19 (0.08)	0.86!: 267 (26.7%) 1.04!: 249 (24.9%) 1.05!: 324 (32.4%) 1.06!: 160 (16.0%) ! rounded	IIIIIIIIIIIII IIIIIIIIIIII IIIIIIIIIIIIIIII IIIIIII	1000 (100%)	0 (0%)

# First save the results

iris_stats_by_species <- by(data = iris, INDICES = iris$Species, FUN = descr, 
    stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)

# Then use view(), like so:

view(iris_stats_by_species, method = "pander", style = "rmarkdown")

Descriptive Statistics

Data Frame: iris
Group: Species = setosa
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	5.01	0.35	4.30	5.00	5.80
Sepal.Width	3.43	0.38	2.30	3.40	4.40
Petal.Length	1.46	0.17	1.00	1.50	1.90
Petal.Width	0.25	0.11	0.10	0.20	0.60

Group: Species = versicolor
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	5.94	0.52	4.90	5.90	7.00
Sepal.Width	2.77	0.31	2.00	2.80	3.40
Petal.Length	4.26	0.47	3.00	4.35	5.10
Petal.Width	1.33	0.20	1.00	1.30	1.80

Group: Species = virginica
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	6.59	0.64	4.90	6.50	7.90
Sepal.Width	2.97	0.32	2.20	3.00	3.80
Petal.Length	5.55	0.55	4.50	5.55	6.90
Petal.Width	2.03	0.27	1.40	2.00	2.50

# view(iris_stats_by_species)

data(tobacco)  # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, by(BMI, age.gr, descr, stats = c("mean", "sd", "min", 
    "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")

Descriptive Statistics

Variable: tobacco$BMI by age.gr

	18-34	35-50	51-70	71 +
Mean	23.84	25.11	26.91	27.45
Std.Dev	4.23	4.34	4.26	4.37
Min	8.83	10.35	9.01	16.36
Median	24.04	25.11	26.77	27.52
Max	34.84	39.44	39.21	38.37

BMI_by_age <- with(tobacco, by(BMI, age.gr, descr, transpose = TRUE, stats = c("mean", 
    "sd", "min", "med", "max")))

view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)

	Mean	Std.Dev	Min	Median	Max
18-34	23.84	4.23	8.83	24.04	34.84
35-50	25.11	4.34	10.35	25.11	39.44
51-70	26.91	4.26	9.01	26.77	39.21
71 +	27.45	4.37	16.36	27.52	38.37

tobacco_subset <- tobacco[, c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)

# view(freq_tables, footnote = NA, file = 'freq-tables.html')

what.is(iris)

$properties property value 1 class data.frame 2 typeof list 3 mode list 4 storage.mode list 5 dim 150 x 5 6 length 5 7 is.object TRUE 8 object.type S3 9 object.size 7256 Bytes

$attributes.lengths names class row.names 5 1 150

$extensive.is [1] “is.data.frame” “is.list” “is.object” “is.recursive” [5] “is.unsorted”

### Frequencies   
**Variable:** tobacco$gender     
**Type:** Factor (unordered)   

|     &nbsp; | Freq | % Valid | % Valid Cum. | % Total | % Total Cum. |
|-----------:|-----:|--------:|-------------:|--------:|-------------:|
|      **F** |  489 |   50.00 |        50.00 |   48.90 |        48.90 |
|      **M** |  489 |   50.00 |       100.00 |   48.90 |        97.80 |
| **\<NA\>** |   22 |         |              |    2.20 |       100.00 |
|  **Total** | 1000 |  100.00 |       100.00 |  100.00 |       100.00 |

Frequencies

Variable: gender
Type: Factor (unordered)

		Valid		Total
gender	Freq	%	% Cumul	%	% Cumul
F	489	50.00	50.00	48.90	48.90
M	489	50.00	100.00	48.90	97.80
<NA>	22			2.20	100.00
Total	1000	100.00	100.00	100.00	100.00

Generated by summarytools 0.8.8 (R version 3.5.1)
2019-01-14

skimr

library(skimr)
skim(df)

DataExplorer

library(DataExplorer)
DataExplorer::create_report(df)

Grafikler

### Cross-Tabulation / Row Proportions   
**Variables:** gender * smoker     
**Data Frame:** tobacco   
   
|        |        |              |              |                |
|-------:|-------:|-------------:|-------------:|---------------:|
|        | smoker |          Yes |           No |          Total |
| gender |        |              |              |                |
|      F |        | 147 (30.06%) | 342 (69.94%) |  489 (100.00%) |
|      M |        | 143 (29.24%) | 346 (70.76%) |  489 (100.00%) |
| \<NA\> |        |   8 (36.36%) |  14 (63.64%) |   22 (100.00%) |
|  Total |        | 298 (29.80%) | 702 (70.20%) | 1000 (100.00%) |

Cross-Tabulation / Row Proportions

Variables: gender * smoker
Data Frame: tobacco

	smoker
gender	Yes	No	Total
F	147 (30.06%)	342 (69.94%)	489 (100.00%)
M	143 (29.24%)	346 (70.76%)	489 (100.00%)
<NA>	8 (36.36%)	14 (63.64%)	22 (100.00%)
Total	298 (29.80%)	702 (70.20%)	1000 (100.00%)

Generated by summarytools 0.8.8 (R version 3.5.1)
2019-01-14

descr(tobacco, style = 'rmarkdown')

print(descr(tobacco), method = 'render', table.classes = 'st-small')

dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')

Here, building up a #ggplot2 as slowly as possible, #rstats. Incremental adjustments. #rstatsteachingideas pic.twitter.com/nUulQl8bPh
— Gina Reynolds (@EvaMaeRey) August 13, 2018

Dreaming of a fancy #Rstats #ggplot #dataviz but still scared of typing #code? @_pvictorr esquisse package has you covered https://t.co/1vIDXcVAAF pic.twitter.com/RlTkptnrNv
— Radoslaw Panczak (@RPanczak) October 2, 2018

Rcmdr

library(Rcmdr)

Rcmdr::Commander()

A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/

jamovi

https://www.jamovi.org/

https://blog.jamovi.org/2018/07/30/rj.html

Sonraki Konular

RStudio ile GitHub
Hipotez testleri
R Markdown ve R Notebook ile tekrarlanabilir rapor

Diğer kodlar

Diğer kodlar için bakınız: https://sbalci.github.io/

Geri Bildirim

Geri bildirim için tıklayınız: Geri bildirim formu

Bu bir derlemedir, mümkün mertebe alıntılara referans vermeye çalıştım.↩

R ile analize başlarken1

Derleyen Serdar Balcı, MD, Pathologist

2019-01-14

R hakkında

R yükleme

R-project

RStudio

RStudio eklentileri

X11

Java OS

R zor şeyler için kolay, kolay şeyler için zor

R paketleri

Neden paketler var

Paketleri nereden bulabiliriz

Kendi paket evrenini oluştur

R için yardım bulma

R paket yükleme

R studio ile proje oluşturma

RStudio ile veri yükleme

Excel

SPSS

csv

Veriyi görüntüleme

Veriyi değiştirme

Veriyi kod ile değiştirelim

Veriyi eklentilerle değiştirme

RStudio aracılığıyla recode

Basit tanımlayıcı istatistikler

summarytools

Frequencies

Cross-Tabulation / Row Proportions

Descriptive Statistics

Data Frame Summary

Descriptive Statistics

Descriptive Statistics

Frequencies

skimr

DataExplorer

Grafikler

Cross-Tabulation / Row Proportions

Rcmdr

jamovi

Sonraki Konular

Diğer kodlar

Geri Bildirim

R ile analize başlarken¹